Published 2/2026
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.73 GB | Duration: 16h 22m
Build interactive data apps with Streamlit & Python, from basics to deployment, using real-world projects and dashboards
What you’ll learn
Build interactive, production-ready data applications using Streamlit and Python
Design clean and responsive Streamlit layouts with reusable components
Capture and manage user inputs, events, and session state effectively
Visualize data using tables, metrics, charts, and interactive plots
Create multi-page Streamlit applications with shared state and navigation
Optimize app performance using caching and state management techniques
Integrate Streamlit apps with databases and external APIs
Customize UI using themes, CSS, and branding techniques
Deploy Streamlit applications to cloud and production environments
Build and deploy real-world projects, including a Personal Finance Tracker & Budget Planner
Requirements
Enthusiasm and determination to make your mark on the world!
Description
A warm welcome to Streamlit with Python: Build and Deploy Real-World Data Apps course by Uplatz.
Streamlit is an open-source Python framework that lets you build interactive web apps for data, analytics, and machine learning—using only Python
No HTML, CSS, or JavaScript required. If you can write a Python script, you can build a web app.
It’s widely used by data scientists, analysts, ML engineers, and Python developers to turn scripts and notebooks into shareable apps in minutes.
How Streamlit Works
Streamlit follows a script-based execution model
You write a normal Python script
You use st * commands (like st button, st dataframe, st line_chart)
Streamlit runs your script top to bottom
Every user interaction (button click, slider move) re-runs the script
Streamlit automatically updates the UI in the browser
Key Idea
Your Python script is your web app
No routes, no callbacks, no frontend state headaches.
Behind the Scenes (What Happens Internally)
Python code runs on the backend
Streamlit
Detects UI elements
Sends UI state to the browser
Re-executes the script on interaction
Session state keeps track of user-specific data
Caching prevents unnecessary recomputation
This makes Streamlit
Extremely fast to develop
Easy to reason about
Ideal for data-driven apps
Main Features of Streamlit
1. Rapid App Development
Build apps in minutes, not days
No frontend knowledge required
Minimal boilerplate code
2. Rich UI Components
Out of the box support for
Text, markdown, metrics
Buttons, sliders, checkboxes
Forms and input widgets
Tables and editable dataframes
3. Powerful Data Visualization
Native charts (st line_chart, st bar_chart)
Full support for
Matplotlib
Seaborn
Plotly
Altair
Interactive dashboards with minimal code
4. Session State & Caching
st session_state for user-specific data
Caching for
Data loading
Expensive computations
Major performance boost for real apps
5. Multi-Page Applications
Build multi-page dashboards
Shared navigation and state
Clean project structure for large apps
6. File Handling & Media Support
Upload CSV, Excel, images, audio, video
Download processed files
Great for tools and internal utilities
7. Database & API Integration
Connect to
SQL databases
Cloud databases
REST APIs
Build fully data-driven applications
8. Styling & Theming
Built-in themes
Custom CSS injection
Branding-ready UIs
9. Easy Deployment
Streamlit Community Cloud
Docker
AWS, Azure, GCP
Works well with CI/CD pipelines
What Streamlit Is Best For
Data dashboards
ML model demos
Internal tools
Analytics apps
Rapid prototypes
Personal or startup projects
Not ideal for
Heavy frontend animations
Complex SPA-style apps
Highly custom UI logic
Streamlit lets you turn Python scripts into interactive web apps with zero frontend code.
Why Take This Streamlit Course?
Streamlit is one of the fastest ways to turn Python code into real, usable applications. This course focuses on practical, real-world usage, not just isolated features.
You won’t just learn Streamlit—you’ll build complete applications, understand production best practices, and confidently deploy your apps.
This course is designed to help you move from
Python scripts ➜ interactive web apps
Notebooks ➜ shareable dashboards
Ideas ➜ deployable products
Course Overview
This course takes a hands-on, project-driven approach to Streamlit.
You’ll start with Streamlit fundamentals and gradually move into
UI layout and interactivity
Data visualization and editable data apps
State management and performance optimization
Multi-page app architecture
Database and API integrations
Styling, theming, and branding
Deployment and production workflows
Each concept is explained with clear examples and then applied to real-world use cases.
Hands-On Projects Included
Throughout the course, you’ll build practical applications, including
Interactive data dashboards
Multi-page Streamlit applications
Data editing and validation tools
API-driven data apps
Production-ready deployed apps
Capstone Projects
End-to-End Streamlit Capstone Application
Personal Finance Tracker & Budget Planner
These projects reinforce everything you learn and can be added to your portfolio or GitHub.
What Makes This Course Different
Focus on real-world app building, not toy examples
Covers deployment and production, not just development
Includes multi-page apps and state management
Ideal balance of simplicity + professional practices
Beginner-friendly but still valuable for experienced developers
How This Course Is Taught
Clear, step-by-step explanations
Hands-on coding demonstrations
Practical examples over theory
Real-world project workflows
Clean, structured progression
You’ll always understand why something is used—not just how.
After Completing This Course, You’ll Be Able To
Build interactive data apps using Streamlit and Python
Design clean, user-friendly Streamlit interfaces
Manage application state and performance efficiently
Create multi-page Streamlit applications
Integrate databases and APIs into your apps
Deploy Streamlit apps to cloud and production environments
Confidently showcase Streamlit projects professionally
Streamlit with Python: Build and Deploy Real-World Data Apps – Course Curriculum
Module 1: Getting Started with Streamlit
What is Streamlit and Why It Matters
Installing Streamlit and Environment Setup
Running Your First Streamlit App
Understanding the Streamlit App Lifecycle
Module 2: Core Components and App Layout
Streamlit Page Structure
Text, Markdown, and Media Elements
Layout Control with Containers, Columns, and Expanders
Best Practices for Clean App Design
Module 3: User Input Widgets and Interactivity
Buttons, Sliders, Checkboxes, and Radio Buttons
Text Inputs and Select Boxes
Forms and User Interaction Flow
Handling User Events Effectively
Module 4: Data Visualization with Streamlit
Displaying Tables and Metrics
Plotting with Matplotlib and Seaborn
Interactive Charts with Plotly
Choosing the Right Visualization for Your Data
Module 5: Advanced DataFrames and Editors
Displaying Large DataFrames Efficiently
Using st data_editor
Editable Tables and Validation
Real-World Data Editing Scenarios
Module 6: State Management and Caching
Understanding Session State
Managing User Sessions
Caching Data and Functions
Performance Optimization Techniques
Module 7: Specialized Streamlit Features
File Uploads and Downloads
Media Handling (Images, Audio, Video)
Progress Bars and Status Messages
Custom Components Overview
Module 8: Building Multi-Page Streamlit Applications
Creating Multi-Page App Structures
Navigation and Page Routing
Sharing State Across Pages
Designing Scalable App Architectures
Module 9: Styling, Themes, and UI Customization
Custom Themes and Layout Styling
Using CSS with Streamlit
Branding Your Streamlit App
Improving UX and Visual Appeal
Module 10: Database and API Integration
Connecting Streamlit to Databases
Working with SQL Queries
Consuming REST APIs
Building Data-Driven Applications
Module 11: Deployment and Production – Part 1
Preparing Streamlit Apps for Deployment
Environment Configuration
Secrets Management
Common Deployment Pitfalls
Module 12: Deployment and Production – Part 2
Deploying on Streamlit Cloud
Deploying on Cloud Platforms (AWS / GCP / Azure Overview)
Performance and Scaling Considerations
Monitoring and Maintenance
Module 13: Capstone Project – End-to-End Streamlit Application
Project Planning and Architecture
Building a Complete Production-Grade App
Applying Best Practices Learned
Final Review and Enhancements
Module 14: Real-World Project – Personal Finance Tracker & Budget Planner
Designing the Finance Tracker
Expense Tracking and Budget Logic
Data Visualization and Insights
Deploying the Final Project
Who this course is for
Python developers who want to build interactive web applications without learning frontend frameworks
Data analysts and data scientists looking to convert notebooks into shareable, production-ready dashboards
Machine learning practitioners who want to deploy models as simple web apps
Business analysts and professionals who want to create data-driven tools and internal dashboards
Students and beginners in data analytics or Python seeking hands-on, project-based learning
Startup founders and product builders who want to quickly prototype data applications
Anyone interested in building dashboards, tools, or internal apps using Python
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